Key Insights:
- The proportion of associate degree, bachelor’s degree, master’s degree, and doctorate graduates aged 25 and above increased significantly from 9.8% in 2008 to 24.6% in 2023.
- Similarly, the proportion of secondary education and above graduates rose from 26.5% in 2008 to 48.3% in 2023.
Analysis:
- This significant improvement indicates increased access to higher education opportunities in Turkey over the past 15 years.
- Policy reforms, educational infrastructure improvements, and increased participation of women in higher education likely contributed to this growth.
- Despite improvements, nearly 75.4% of the population aged 25 and above in 2023 still do not hold a university degree, indicating room for further growth.
Implications:
- Policies should focus on bridging regional disparities and supporting economically disadvantaged groups to access higher education.
- Investments in lifelong learning programs can target the adult population with limited formal education.
📚 Average Duration of Education (2023)
Key Insights:
- In 2023, the average duration of education for individuals aged 25 and over was 9.3 years. - Gender Gap:
- Women: 8.6 years
- Men: 10.1 years
✅ En Yüksek Ortalama Eğitim Süresi Olan İller:
İl Adı Education_Avg
1235 Ankara 10.993782
1263 İstanbul 10.468269
1255 Eskişehir 10.443567
1264 İzmir 10.324897
1306 Yalova 10.249052
✅ En Düşük Ortalama Eğitim Süresi Olan İller:
İl Adı Education_Avg
1292 Şanlıurfa 8.283931
1233 Ağrı 8.355252
1278 Muş 8.625626
1276 Mardin 8.777682
1294 Van 8.847596
✅ En Yüksek Eğitim Süresi Değişim Oranı Olan İller:
İl Adı_ Türkiye Change_Rate
58 Tekirdağ 29.875646
76 Yalova 26.885635
40 Kocaeli 23.671948
62 Şanlıurfa 23.353189
78 Kilis 22.174356
✅ En Düşük Eğitim Süresi Değişim Oranı Olan İller:
İl Adı_ Türkiye Change_Rate
74 Ardahan -6.134902
35 Kars -2.878420
3 Ağrı -2.677055
65 Yozgat -2.126473
43 Malatya -1.198064
📈 Literacy Rates (2008–2023)
Key Insights:
- The literacy rate among individuals aged 6 years and over increased from 91.8% in 2008 to 97.6% in 2023.
- Women: Improved from 86.9% to 96.0%.
- Men: Improved from 96.7% to 99.2%.
Analysis:
- The significant improvement in women’s literacy rates suggests a successful reduction in gender disparity in education.
- Despite the progress, the remaining 4% illiteracy rate among women indicates persistent cultural and socio-economic barriers in certain regions.
Implications:
- Adult literacy programs targeting women in rural and underserved areas remain critical.
- Efforts should focus on ensuring literacy for the remaining 4% of the population, particularly women in remote areas.
👩🎓👨🎓 Parental Education Influence
Key Insights: - Mothers with Higher Education:
- 84.6% of their children completed higher education.
- 12.5% completed secondary education.
- 2.9% completed lower secondary education. - Fathers with Higher Education:
- 80.2% completed higher education.
- 16.0% completed secondary education.
- 3.8% completed lower secondary education.
Analysis:
- There is a strong correlation between parental education levels and children’s educational outcomes. - Mothers’ education level appears to have a slightly stronger influence than fathers’.
Implications:
- Supporting parental education initiatives can break the cycle of low educational attainment.
- Programs targeting parents in low-education regions could indirectly improve children’s education levels.
📝 General Recommendations
Focus on Regional Disparities: Prioritize investment in provinces with lower education durations.
Close the Gender Gap: Launch gender-specific education campaigns.
Adult Education Programs: Expand literacy and education programs.
Parental Education Awareness: Implement campaigns for parental influence in education.
Conclusion: Significant progress has been made, but regional disparities, gender gaps, and parental influence remain key areas for intervention. A strategic, inclusive approach will drive sustainable improvements in Turkey’s education landscape.
---title: "Educational Trends in Turkey (2008–2023)"format: html---# 🚀 Higher Education Attainment Trends (2008–2023)**Key Insights:** - The proportion of **associate degree, bachelor's degree, master's degree, and doctorate graduates** aged **25 and above** increased significantly from **9.8% in 2008** to **24.6% in 2023**. - Similarly, the proportion of **secondary education and above graduates** rose from **26.5% in 2008** to **48.3% in 2023**.**Analysis:** - This significant improvement indicates **increased access to higher education opportunities** in Turkey over the past 15 years. - Policy reforms, educational infrastructure improvements, and increased participation of women in higher education likely contributed to this growth. - Despite improvements, nearly **75.4% of the population aged 25 and above in 2023 still do not hold a university degree**, indicating room for further growth.**Implications:** - Policies should focus on **bridging regional disparities** and **supporting economically disadvantaged groups** to access higher education. - Investments in **lifelong learning programs** can target the adult population with limited formal education.---::: {.cell}<img src="gender_inequality_pie_chart_by_level.gif" alt="Kadın-Erkek Farkı Isı Dağılım Grafiği" style="width: 100%; display: block; margin: 0 auto;" />---# 📚 Average Duration of Education (2023)**Key Insights:** - In **2023**, the **average duration of education** for individuals aged **25 and over** was **9.3 years**.- **Gender Gap:** - Women: **8.6 years** - Men: **10.1 years**---```{python}import pandas as pdimport matplotlib.pyplot as plt# Veri setini yükle ve tamamen boş sütunları kaldırdf = pd.read_excel('/home/mami/emu430/emu430-fall2024-team-education_atlas/data/yıllara_göre_egitim.xlsx')df = df.dropna(axis=1, how='all')# İlk iki satırı başlık olarak kullan ve eksik değerleri doldurheader1 = df.iloc[0].bfill()header2 = df.iloc[1].bfill()# Çok seviyeli (MultiIndex) başlıklar oluşturdf.columns = pd.MultiIndex.from_tuples(zip(header1, header2))df = df.iloc[2:].reset_index(drop=True)# İlk 3 sütunu tek seviyeli başlık yapfirst_three_headers = ['Yıl', 'İl Kodu', 'İl Adı']# Ana başlıklar ve alt başlıkları tanımlamain_headers = ['Genel toplam', 'Okuma yazma bilmeyen', 'Okuma yazma bilen fakat bir okul bitirmeyen','İlkokul', 'İlköğretim', 'Ortaokul ve dengi meslek', 'Lise ve dengi meslek','Yüksekokul veya fakülte', 'Yüksek lisans', 'Doktora', 'Bilinmeyen']sub_headers = ['Toplam', 'Erkek', 'Kadın']# Sütunları 3'erli gruplar hâlinde düzenleeducation_columns = []for main in main_headers:for sub in sub_headers: education_columns.append(f"{main}_{sub}")# Tüm başlıkları birleştirfinal_headers = first_three_headers + education_columns[:df.shape[1] -len(first_three_headers)]df.columns = final_headers# Yıl sütununu kontrol etdf['Yıl'] = pd.to_numeric(df['Yıl'], errors='coerce')df = df.dropna(subset=['Yıl'])df['Yıl'] = df['Yıl'].astype(int)# 2023 yılı verilerini çekdf_2023 = df[df['Yıl'] ==2023].copy()# Eğitim düzeylerine karşılık gelen yıl sürelerieducation_years = {'Okuma yazma bilmeyen': 0,'İlkokul': 5,'İlköğretim': 8,'Ortaokul ve dengi meslek': 8,'Lise ve dengi meslek': 12,'Yüksekokul veya fakülte': 16,'Yüksek lisans': 18,'Doktora': 22}# Eğitim süresi hesaplamasıdef calculate_avg_education_duration(row): total_years =0 total_students =0for level, years in education_years.items(): col_name =f"{level}_Toplam"if col_name in row.index and pd.notnull(row[col_name]): total_years += row[col_name] * years total_students += row[col_name]if total_students ==0:return0return total_years / total_students# Ortalama eğitim süresi hesapladf_2023['Education_Avg'] = df_2023.apply(calculate_avg_education_duration, axis=1)# 'Türkiye' satırını çıkardf_2023 = df_2023[~df_2023['İl Adı'].str.contains('Türkiye', case=False, na=False)]# En yüksek ve en düşük ortalamaya sahip illeri bultop5_provinces = df_2023.nlargest(5, 'Education_Avg')[['İl Adı', 'Education_Avg']]bottom5_provinces = df_2023.nsmallest(5, 'Education_Avg')[['İl Adı', 'Education_Avg']]# Sonuçları yazdırprint("✅ En Yüksek Ortalama Eğitim Süresi Olan İller:")print(top5_provinces)print("\n✅ En Düşük Ortalama Eğitim Süresi Olan İller:")print(bottom5_provinces)# Grafik oluşturmaimport matplotlib.pyplot as pltfig, ax = plt.subplots(figsize=(10, 6))ax.bar(top5_provinces['İl Adı'], top5_provinces['Education_Avg'], color='skyblue', label='Highest Average')ax.bar(bottom5_provinces['İl Adı'], bottom5_provinces['Education_Avg'], color='orange', label='Lowest Average')ax.set_xlabel('Provinces')ax.set_ylabel('Average Education Duration (Years)')ax.set_title('Highest and Lowest Average Education Duration by Province (2023)')ax.legend()plt.xticks(rotation=30, ha='right')plt.tight_layout()plt.show()```---```{python}import pandas as pdimport matplotlib.pyplot as plt# Veri setini yükle ve tamamen boş sütunları kaldırdf = pd.read_excel('/home/mami/emu430/emu430-fall2024-team-education_atlas/data/yıllara_göre_egitim.xlsx')df = df.dropna(axis=1, how='all')# İlk iki satırı başlık olarak kullan ve eksik değerleri doldurheader1 = df.iloc[0].bfill()header2 = df.iloc[1].bfill()# Çok seviyeli (MultiIndex) başlıklar oluşturdf.columns = pd.MultiIndex.from_tuples(zip(header1, header2))df = df.iloc[2:].reset_index(drop=True)# Çok seviyeli sütunları birleştirdf.columns = ['_'.join(map(str, col)).strip() ifisinstance(col, tuple) else col for col in df.columns]# Yıl sütununu bul ve adını kontrol etyear_column = [col for col in df.columns if'Yıl'in col or'Unnamed: 0'in col]ifnot year_column:raiseKeyError("Veri setinde 'Yıl' sütunu bulunamadı.")year_column = year_column[0] # İlk eşleşeni al# Yıl sütununu düzenledf['Yıl'] = pd.to_numeric(df[year_column], errors='coerce')df = df.dropna(subset=['Yıl'])df['Yıl'] = df['Yıl'].astype(int)# İl Adı sütununu kontrol etprovince_column = [col for col in df.columns if'İl Adı'in col or'Unnamed: 2'in col]ifnot province_column:raiseKeyError("Veri setinde 'İl Adı' sütunu bulunamadı.")province_column = province_column[0] # İlk eşleşeni al# 2014 ve 2023 verilerini çekdf_2014 = df[df['Yıl'] ==2014].copy()df_2023 = df[df['Yıl'] ==2023].copy()# Eğitim sürelerini hesaplaeducation_columns = [col for col in df.columns if'Toplam'in col and'Genel toplam'notin col]df_2014['Education_Avg'] = df_2014[education_columns].mean(axis=1)df_2023['Education_Avg'] = df_2023[education_columns].mean(axis=1)# İl bazında ortalama eğitim süresindeki değişim oranını hesapladf_change = pd.merge( df_2014[[province_column, 'Education_Avg']], df_2023[[province_column, 'Education_Avg']], on=province_column, suffixes=('_2014', '_2023'))df_change['Change_Rate'] = ((df_change['Education_Avg_2023'] - df_change['Education_Avg_2014']) / df_change['Education_Avg_2014']) *100# Tür dönüşümü ve geçersiz değerlerin temizlenmesidf_change['Change_Rate'] = pd.to_numeric(df_change['Change_Rate'], errors='coerce')df_change = df_change.dropna(subset=['Change_Rate'])# En yüksek ve en düşük değişim oranına sahip illeri belirletop5_change = df_change.nlargest(5, 'Change_Rate')bottom5_change = df_change.nsmallest(5, 'Change_Rate')# Sonuçları yazdırprint("✅ En Yüksek Eğitim Süresi Değişim Oranı Olan İller:")print(top5_change[[province_column, 'Change_Rate']])print("✅ En Düşük Eğitim Süresi Değişim Oranı Olan İller:")print(bottom5_change[[province_column, 'Change_Rate']])# Grafik oluşturmafig, ax = plt.subplots(figsize=(12, 7))# En yüksek değişim oranlarıax.bar(top5_change[province_column], top5_change['Change_Rate'], color='green', label='Highest Improvement')# En düşük değişim oranlarıax.bar(bottom5_change[province_column], bottom5_change['Change_Rate'], color='red', label='Lowest Improvement')# Grafik detaylarıax.set_title('Highest and Lowest Improvement in Education Duration (2014–2023)')ax.set_xlabel('Provinces')ax.set_ylabel('Change Rate (%)')ax.legend()plt.xticks(rotation=45, ha='right')plt.tight_layout()plt.show()```---# 📈 Literacy Rates (2008–2023)**Key Insights:** - The literacy rate among individuals aged **6 years and over** increased from **91.8% in 2008** to **97.6% in 2023**. - **Women:** Improved from **86.9%** to **96.0%**. - **Men:** Improved from **96.7%** to **99.2%**.**Analysis:** - The significant improvement in women's literacy rates suggests a **successful reduction in gender disparity** in education. - Despite the progress, the remaining **4% illiteracy rate** among women indicates persistent **cultural and socio-economic barriers** in certain regions.**Implications:** - Adult literacy programs targeting women in rural and underserved areas remain critical. - Efforts should focus on **ensuring literacy for the remaining 4% of the population**, particularly women in remote areas.---::: {.cell}<img src="gender_difference_heatmap.gif" alt="Kadın-Erkek Farkı Isı Dağılım Grafiği" style="width: 100%; display: block; margin: 0 auto;" />---# 👩🎓👨🎓 Parental Education Influence**Key Insights:**- **Mothers with Higher Education:** - 84.6% of their children completed **higher education**. - 12.5% completed **secondary education**. - 2.9% completed **lower secondary education**.- **Fathers with Higher Education:** - 80.2% completed **higher education**. - 16.0% completed **secondary education**. - 3.8% completed **lower secondary education**.**Parents with Secondary Education:** - **Mothers:** 64.3% attained **higher education**. - **Fathers:** 55.4% attained **higher education**.**Parents with Lower Secondary Education:** - **Mothers:** 27.9% attained **higher education**. - **Fathers:** 26.6% attained **higher education**.**Analysis:** - There is a **strong correlation between parental education levels and children's educational outcomes**.- **Mothers' education level** appears to have a slightly stronger influence than fathers'.**Implications:** - Supporting **parental education initiatives** can break the cycle of low educational attainment. - Programs targeting parents in low-education regions could indirectly improve children's education levels.---# 📝 General Recommendations1. **Focus on Regional Disparities:** Prioritize investment in provinces with lower education durations.2. **Close the Gender Gap:** Launch **gender-specific education campaigns**.3. **Adult Education Programs:** Expand literacy and education programs.4. **Parental Education Awareness:** Implement campaigns for **parental influence** in education.5. **Sustain Urban Improvements:** Emphasize **lifelong learning programs**.**Conclusion:** Significant progress has been made, but **regional disparities, gender gaps, and parental influence** remain key areas for intervention. A strategic, inclusive approach will drive sustainable improvements in Turkey's education landscape.```{python}import pandas as pd# Veri setini yükle ve tamamen boş sütunları kaldırdf = pd.read_excel('/home/mami/emu430/emu430-fall2024-team-education_atlas/data/yıllara_göre_egitim.xlsx')df = df.dropna(axis=1, how='all')# Yılları ve indeks aralıklarını tanımlayears =list(range(2008, 2024)) # 2008'den 2023'e kadarindex_ranges = [(1+ i *82, 1+ (i +1) *82) for i inrange(len(years))]# Her yıl için alt veri çerçevelerini oluşturyearly_data = {}for year, (start, end) inzip(years, index_ranges): yearly_data[year] = df.iloc[start:end]# İlk birkaç yılın veri çerçevelerini kontrol etsample_years = {year: data.head(1) for year, data inlist(yearly_data.items())[:3]}# İlk iki satırı başlık olarak kullan ve eksik değerleri doldurheader1 = df.iloc[0].bfill()header2 = df.iloc[1].bfill()# Çok seviyeli (MultiIndex) başlıklar oluşturdf.columns = pd.MultiIndex.from_tuples(zip(header1, header2))# İlk iki satırı kaldır ve veri setini sıfırladf = df.iloc[2:].reset_index(drop=True)# İlk 3 sütun için tek seviyeli başlıklar oluşturfirst_three_headers = ['Yıl', 'İl Kodu', 'İl Adı']# Ana başlıklar ve alt başlıkları tanımlamain_headers = ['Genel toplam', 'Okuma yazma bilmeyen', 'Okuma yazma bilen fakat bir okul bitirmeyen','İlkokul', 'İlköğretim', 'Ortaokul ve dengi meslek', 'Lise ve dengi meslek','Yüksekokul veya fakülte', 'Yüksek lisans', 'Doktora', 'Bilinmeyen']sub_headers = ['Toplam', 'Erkek', 'Kadın']# Çok seviyeli başlıkları oluşturremaining_headers = [(main, sub) for main in main_headers for sub in sub_headers]# Tüm başlıkları birleştirfinal_headers = first_three_headers + remaining_headers[:df.shape[1] -len(first_three_headers)]df.columns = pd.MultiIndex.from_tuples( [(header, '') ifisinstance(header, str) else header for header in final_headers])ifisinstance(df.columns, pd.MultiIndex): df.columns = ['_'.join(map(str, col)).strip() ifisinstance(col, tuple) else col for col in df.columns]# Yıl sütununu bulyear_column = [col for col in df.columns if'Yıl'in col]ifnot year_column:raiseKeyError("Veri setinde 'Yıl' sütunu bulunamadı.")year_column = year_column[0]import matplotlib.pyplot as pltimport pandas as pdimport numpy as npimport seaborn as snsfrom PIL import Imageimport osimport plotly.express as pximport plotly.graph_objects as goimport jsondef create_turkey_interactive_map(df):""" Türkiye haritası üzerinde şehir bazında kadın-erkek farkını ve belirli başlıklardaki cinsiyet eşitsizlik indekslerini gösteren etkileşimli bir harita oluşturur. """ city_column = [col for col in df.columns if'İl Adı'in col][0] year_column = [col for col in df.columns if'Yıl'in col][0] years = df[year_column].dropna().unique()# JSON dosyasını doğrula geojson_path ='data/tr-cities.json'ifnot os.path.exists(geojson_path):raiseFileNotFoundError("GeoJSON dosyası bulunamadı: data/tr-cities.json")withopen(geojson_path) as f: geojson = json.load(f)# Veri temizliği ve eşleştirme df = clean_turkey_city_names(df) df = map_city_numbers(df, geojson) df = calculate_gender_inequality_indices(df) dropdown_options = [ {'label': str(year), 'method': 'update', 'args': [{'visible': [year == y for y in years]}, {'title': f'Cinsiyet Eşitsizlik İndeksi - {year}'}]}for year in years ] fig = go.Figure()for year in years: df_year = df.loc[df[year_column] == year].copy()# Hover metni için gerekli bilgiler df_year['hover_text'] = ('Şehir: '+ df_year[city_column] +'<br>'+'Genel Toplam Eşitsizlik: '+ df_year['Genel toplam_Eşitsizlik'].astype(str) +'<br>'+'Okuma Yazma Bilmeyen: '+ df_year['Okuma yazma bilmeyen_Eşitsizlik'].astype(str) +'<br>'+'Yüksekokul veya Fakülte: '+ df_year['Yüksekokul veya fakülte_Eşitsizlik'].astype(str) )# Harita katmanı oluştur fig.add_trace( go.Choropleth( geojson=geojson, locations=df_year['CityNumber'], z=df_year['Genel toplam_Eşitsizlik'], text=df_year['hover_text'], hoverinfo='text', featureidkey='properties.number', colorscale='YlGnBu', # Düşük eşitsizlik: Yeşil | Yüksek eşitsizlik: Kırmızı zmin=0, zmax=df_year['Genel toplam_Eşitsizlik'].max(), colorbar_title='Eşitsizlik İndeksi', visible=(year == years[0]) ) ) fig.update_layout( title_text='Yıla Göre Cinsiyet Eşitsizlik İndeksi Haritası', geo=dict( fitbounds="locations", visible=True ), updatemenus=[{'buttons': dropdown_options, 'direction': 'down', 'showactive': True}] ) fig.show()def clean_turkey_city_names(df):""" Excel veri çerçevesindeki şehir adlarını temizler ve gerekli düzeltmeleri yapar. """# 'Türkiye' ve NaN satırlarını kaldır df = df[~df['İl Adı_'].isin(['türkiye'])] df = df.dropna(subset=['İl Adı_'])# Özel şehir adı düzeltmeleri city_replacements = {'Afyonkarahisar': 'Afyon','i̇çel': 'mersin' } df['İl Adı_'] = df['İl Adı_'].replace(city_replacements)return dfdef map_city_numbers(df, geojson):""" Şehir adlarını şehir numaralarıyla eşleştirir. """ city_mapping = {feature['properties']['name'].strip().lower(): str(feature['properties']['number']) for feature in geojson['features']} df['İl Adı_'] = df['İl Adı_'].str.strip().str.lower() df['CityNumber'] = df['İl Adı_'].map(city_mapping)# Eşleşmeyen şehirleri kontrol et unmatched_cities = df.loc[df['CityNumber'].isnull(), 'İl Adı_']#if not unmatched_cities.empty:# print("⚠️ Hâlâ eşleşmeyen şehirler var:")# print(unmatched_cities.unique())return dfdef calculate_gender_inequality_indices(df):""" Her ana başlık için cinsiyet eşitsizlik indekslerini hesaplar ve veri çerçevesine ekler. """ main_headers = ['Genel toplam', 'Okuma yazma bilmeyen', 'Okuma yazma bilen fakat bir okul bitirmeyen','İlkokul', 'İlköğretim', 'Ortaokul ve dengi meslek', 'Lise ve dengi meslek','Yüksekokul veya fakülte', 'Yüksek lisans', 'Doktora', 'Bilinmeyen' ]for header in main_headers: female_col =f"{header}_Kadın" male_col =f"{header}_Erkek" total_col =f"{header}_Toplam"if female_col in df.columns and male_col in df.columns and total_col in df.columns: df[f"{header}_Eşitsizlik"] = (df[female_col] - df[male_col]).abs() / df[total_col]return df# Veri temizliği ve harita oluşturmadf = clean_turkey_city_names(df)create_turkey_interactive_map(df)```
